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• W2940805846 abstract "Deep convolutional neural networks perform better on images containing spatially invariant noise (synthetic noise); however, their performance is limited on real-noisy photographs and requires multiple stage network modeling. To advance the practicability of denoising algorithms, this paper proposes a novel single-stage blind real image denoising network (RIDNet) by employing a modular architecture. We use a residual on the residual structure to ease the flow of low-frequency information and apply feature attention to exploit the channel dependencies. Furthermore, the evaluation in terms of quantitative metrics and visual quality on three synthetic and four real noisy datasets against 19 state-of-the-art algorithms demonstrate the superiority of our RIDNet." @default.
• W2940805846 created "2019-05-03" @default.
• W2940805846 creator A5072837153 @default.
• W2940805846 creator A5084633048 @default.
• W2940805846 date "2019-04-16" @default.
• W2940805846 modified "2023-10-18" @default.
• W2940805846 title "Real Image Denoising with Feature Attention" @default.
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• W2940805846 hasPublicationYear "2019" @default.
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